Fox News chief national security correspondent Jennifer Griffin reports on what the US and Israel are doing to stay ahead of adversaries in AI on'Special Report.' OpenAI has partnered with a new AI initiative led by a group co-founded with outgoing Special Presidential Envoy for Climate John Kerry that has pushed left-wing causes and has several board members aligned with Democrats. OpenAI, led by CEO Sam Altman, is backing an initiative known as AI 2030, which is aimed at shaping "public dialogue about U.S. competition against China on AI," Politico reported in October. The initiative is led by the "non-partisan" think tank American Security Project (ASP), where Kerry was a founding member and served two stints on the board of directors. ASP has promoted the idea that climate change is a national security threat, and argued on its website that pulling out of the Iran Nuclear Deal was a bad idea that "harms national security."

Fox News congressional correspondent Aishah Hasnie has more on who will be in attendance and policies President-elect Donald Trump will enact during his first day in office on'Special Report.' Democratic lawmakers have penned a letter accusing Big Tech companies and leaders of engaging in an "effort to influence and sway" the incoming administration following substantial donations to President-elect Donald Trump's inaugural fund. The letter, obtained by Fox News Digital, was distributed by Sen. Elizabeth Warren and Sen. Michael Bennet to Amazon, Apple, Google, OpenAI, Meta, Microsoft and Uber. "Big Tech companies have come under increased scrutiny from federal regulators for antitrust violations, violations of privacy, and harms to workers, consumers, and competition. At the same time, lawmakers in both parties have voiced support for regulating tech platforms, in recognition that there is currently no comprehensive set of rules for the tech sector," the letter states.

Due to the broad range of applications of multi-agent reinforcement learning (MARL), understanding the effects of adversarial attacks against MARL model is essential for the safe applications of this model. Motivated by this, we investigate the impact of adversarial attacks on MARL. In the considered setup, there is an exogenous attacker who is able to modify the rewards before the agents receive them or manipulate the actions before the environment receives them. The attacker aims to guide each agent into a target policy or maximize the cumulative rewards under some specific reward function chosen by the attacker, while minimizing the amount of the manipulation on feedback and action. We first show the limitations of the action poisoning only attacks and the reward poisoning only attacks.

As part of an ongoing investigation into X, the European Commission has requested documents from the company related to how its recommendation systems work. The European Union's regulatory arm is particularly interested in any recent changes to the algorithm. The EC said it asked X to provide the information by February 15 as it steps up the Digital Services Act (DSA) probe. On top of that, regulators asked for access to certain APIs that X provides so it can conduct "direct fact-finding on content moderation and virality of accounts." The Commission has also slapped X with a retention order.

UK Prime Minister Keir Starmer has signed a 100-year partnership agreement with Ukraine to provide support across various sectors, including healthcare and military technology, while pledging to provide security guarantees if an end to Russia's war comes. During Starmer's first visit to Kyiv since becoming prime minister, the British leader told a news conference on Thursday that the United Kingdom would examine "the practical ways to get a just and lasting peace … that guarantees your security, your independence and your right to choose your own future". "We will work with you and all of our allies on steps that would be robust enough to guarantee Ukraine's security," Starmer said. "Those conversations will continue for many months ahead." While Starmer was speaking with Ukrainian President Volodymyr Zelenskyy at the presidential palace, loud blasts and air raid sirens were heard over Kyiv as air defence systems took aim at a Russian drone attack. The British leader said the Russian attack served as a reminder of the situation on the ground.

The Association for the Advancement of Artificial Intelligence's 2024 Fall Symposium Series was held at Westin Arlington Gateway, Arlington, Virginia, November 7-9, 2024. There were seven symposia in the fall program: AI Trustworthiness and Risk Assessment for Challenging Contexts (ATRACC), Artificial Intelligence for Aging in Place, Integrated Approaches to Computational Scientific Discovery, Large Language Models for Knowledge Graph and Ontology Engineering (LLMs for KG and OE), Machine Intelligence for Equitable Global Health (MI4EGH), Unifying Representations for Robot Application Development, Using AI to Build Secure and Resilient Agricultural Systems: Leveraging AI to mitigate Cyber, Climatic and Economic Threats in Food, Agricultural, and Water (FAW) Systems. This report contains summaries of the workshops, which were submitted by some, but not all, of the workshop chairs. The rapid embrace of AI-based critical systems introduces new dimensions of errors that induce increased levels of risk, limiting trustworthiness. Thus, AI-based critical systems must be assessed across many dimensions by different parties (researchers, developers, regulators, customers, insurance companies, end-users, etc.) for different reasons. Assessment of trustworthiness should be made at both the full system level and at the level of individual AI components. The focus of this symposium was on AI trustworthiness broadly and methods that help provide bounds for fairness, reproducibility, reliability, and accountability in the context of quantifying AI-system risk, spanning the entire AI lifecycle from theoretical research formulations all the way to system implementation, deployment, and operation. This first AAAI symposium on AI Trustworthiness and Risk Assessment in Challenging Contexts was triggered by two initiatives on responsible and trustworthy AI that came together thanks to encouragement given by AAAI: an international community (mostly European and Asia-South Pacific) around AI trustworthiness assessment for critical systems, already gathered at the AITA SSS Symposium in 2023; and a US-based community around University of West Florida, gathered about the question of AI risk assessment in challenging contexts e.g., for security or defense applications.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. The National Transportation Safety Board released its preliminary report on Thursday into what went wrong at a Florida drone show last month that caused some of the aircraft to go rogue, leaving a little boy seriously injured. The mishap took place during a Christmas light show put on by Sky Elements at Lake Eola Park in Orlando on Dec. 21, 2024. Hundreds of people were watching the aerial show when several of the drones flew out of formation – some colliding with one another before falling to the ground. One of the rogue drones struck a 7-year-old boy in the face and chest, knocking him out upon impact.

Unmanned Aerial Vehicles (UAVs), commonly known as Drones, are one of 21st century most transformative technologies. Emerging first for military use, advancements in materials, electronics, and software have catapulted drones into multipurpose tools for a wide range of industries. In this paper, we have covered the history, taxonomy, architecture, navigation systems and branched activities for the same. It explores important future trends like autonomous navigation, AI integration, and obstacle avoidance systems, emphasizing how they contribute to improving the efficiency and versatility of drones. It also looks at the major challenges like technical, environmental, economic, regulatory and ethical, that limit the actual take-up of drones, as well as trends that are likely to mitigate these obstacles in the future. This work offers a structured synthesis of existing studies and perspectives that enable insights about how drones will transform agriculture, logistics, healthcare, disaster management, and other areas, while also identifying new opportunities for innovation and development.

The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90\%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. In addition, we also proposed a Generative AI-assisted interpretation concept based on prompt engineering as a preliminary study of the role of Generative AI agents in covert communication.

This paper proposes a Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX preprocesses table data by focusing on highlighted cells and then generates summary sentences structured with a Theme Part in the form of adverbial phrases followed by an Explanation Part in the form of clauses. In this process, customized analysis is performed by considering the structural characteristics and comparability of the table. Additionally, by utilizing In-Context Learning, Tabular-TX optimizes the analytical capabilities of large language models (LLMs) without the need for fine-tuning, effectively handling the structural complexity of table data. Results from applying the proposed Tabular-TX to generate table-based summaries demonstrated superior performance compared to existing fine-tuning-based methods, despite limitations in dataset size. Experimental results confirmed that Tabular-TX can process complex table data more effectively and established it as a new alternative for table-based question answering and summarization tasks, particularly in resource-constrained environments.